Text in English:

A few weeks ago, I was at the Open Days of the Delft
University of Technology (TU Delft). I made a visit to the
Faculty of Civil
Engineering and Geosciences, where I was introduced to several research
projects. What struck me was how extensively researchers are measuring things.
Carefully, and over a long period. Not without reason, because the researchers
want to know exactly what is going on.

I will not elucidate on the
purpose and the nature of the research projects. The thing is, when I walked
home, I thought about the emergence of the Internet of Things (IoT), which from its contemporary definition
is: the network of physical objects or ‘things’ embedded with
electronics, software, sensors, and network connectivity, which enables these
objects to collect and exchange data. In short: devices that can exchange
information over the internet. Much of the data will come from sensors. Small,
measuring instruments that, for example, measure the temperature of your home
in order to regulate the thermostat of your heater; or an anemometer that will
raise the blinds when a storm is coming. Or perhaps a sensor that reads the
contents of your refrigerator, or detects the type of laundry you’ve put in a
washing machine. Besides these everyday examples there are numerous sensors in
the public space: traffic counters, speed indicators, fog detectors. Even in
your car. Self-driving cars cannot ride without sensors. The future vision is
that more and more devices will get sensors and these devices, with all their
data, can operate and decide independently. And they’re going to share that
information with other devices or applications. For example, your refrigerator
could keep stock by itself and backorder missing items automatically.

All these sensors will exchange their data on the
Internet of Things. If you design or buy systems based on IoT, you will also have to take the behavior of the
sensors into account. All those sensors and other measuring instruments can
sometimes transmit wrong data. They will fail or make mistakes. How do we deal
with that?

So while I walked home, I thought: if the
researchers at TU Delft measures so extensively,
carefully and with so much trouble, will this also happen in my home in the
same way? Will the sensors work as I wish? And what will happen when something
gets broken or goes wrong? Will my refrigerator just haphazardly order
stuff?

Incorrect measurements

Six out of ten fire alarms in the
Netherlands are false. Fire departments are not happy. And these false alarms
are not from people who’re simply calling the emergency services, but from
automatic fire detectors that produce false alarms. You could assume that fire
detectors stay on the safe side. But often fire detectors are incorrectly
adjusted. Or measure the wrong phenomena: where there’s smoke, there’s fire;
but where there’s fire, there’s not a fire for sure. Sneaky cigarette smoking
can be sufficient to set off the alarm. Fire detectors are not perfect. One and
a half years ago, a manufacturer had to recall 400,000 smart smoke
detectors because of defects. Which led to the question: can we build an
IoT ecosystem around such devices?

“With billions of devices, even if a given sensor
gives a bit error once per year, that becomes a much bigger issue in a large
system.” (W.T. Dixon)

My cycle computer makes mistakes. Cycle computers
are also part of the IoT. For example,
I track my physical condition with a training app. The bike computer measures
the height in the countryside where I ride based on air pressure. The higher
you get, the lower the air pressure. But air pressure can also change with the
weather. On a bad day, you can get up to an error of 100 meters in the
altitude measurement. Once I returned from a circular ride, and discovered my
house had risen 60 meters compared with the time I left. At least,
according to my bike computer. This was quite funny, but the measurement of my
cycle route was wrong making my performance indicators also wrong. Air pressure
and height are definitely related, but not completely. There are distorting
influences.

Failing sensors

Every engineer knows that all things will eventually
break down; they exhibit failure behavior. The device on which you read this
article, will also break down some time. As will sensors. But before they
really are out-of-order, they will produce incorrect data. Sensors can also
transmit incorrect measurements because they get dirty, obsolete or worn out.
Researchers, such as at the TU Delft, check and calibrate their
sensors regularly. But will this also happen in the sensors that are built in
all those everyday devices? Do you calibrate your thermostat, the GPS in your car, the altimeter of your bike
computer? Do you regularly clean the external thermometer of your heater? Do
you check your smoke detectors?

In a professional and industrial environment, the
sensors will be maintained, at least, that’s what I hope. But at home, or in
non-technical environments, the maintenance will not happen that often. But the
measured data from these sensors will be used to control machines, to draw
conclusions about your behavior, your health, or your environment, about where
you’ve been and perhaps about your identity.

There are authors who see unreliable sensors as a real
problem for the IoT. When one sensor
generates only a few false measurements per year, this will increase to an
enormous amount of errors when you take the many hundreds of millions of
sensors that constitute the IoT, into
account. And the number of errors will increase when the sensors are not
properly maintained, or when they are of poor quality. Your refrigerator will
not have a built-in sensor of industrial quality, but a simpler and cheaper
type, that’s for sure.

Design with failure behavior

So we should learn from scientists and designers of
reliable IoT systems about the failure behavior of
sensors. And then there is still the failure behavior of other hardware and
software that needs to be considered. Perhaps in a next blog.

To conclude, while certainly not exhaustive, here
are three tips to take error measurement of sensors into account for designing
reliable IoT systems:

Know the failure behavior of the sensors that
you’re going to use. Know the technical specifications and how the sensors do
their measurements. Also, how the environment may affect the measurements. For
example, the altimeter in my bike computer may be calibrated on the basis of known heights on my bicycle
route.

Determine the effects of a malfunctioning sensor.
Is the wrong measurement recognized because it’s an outlier, or because it does
not fit in the model of the system? Does such a sensor induce erratic behavior
of the system or does it even bring everything down? For example, smoke
detectors can be equipped with double sensors, preferably with two different
measuring methods.

Don’t make any conclusions on the basis of a single
measurement. That one can be incorrect. Do multiple measurements over time or
use several different sensors to measure the same thing. This will enable
erroneous measurements to be more easily recognized. For example: if my
outdoor thermometer can compare its measurements with data
about the current weather or other outside thermometers nearby, derogations can
be recognized.